Protection of cultural heritage buildings and artistic assets from seismic hazard: A hierarchical approach

The occurrence of natural disasters such as earthquakes represent a worldwide challenge in the conservation of cultural heritage (CH), which suffer from damage due to high vulnerability conditions. Therefore, the protection of CH from seismic hazard is of paramount importance. Damage and vulnerability assessment of CH and artistic assets play a key role in the identification of conservation strategies. Effective strategies require the stabilization of severely damaged buildings and the preventive improvement of constructions structural response to seismic actions. Although the operation of emergency inspections is meant to classify buildings on the basis of buildings residual seismic capacity, investment decisions in restoration and conservation strategies of such vulnerable structures must take into consideration tangible and intangible values of both building structures and artistic goods as well as must combine objectives of verifying structural safety standards and preserving cultural heritage significance. Damage and vulnerability assessment depend on different criteria, which, on the one hand, are related to buildings structural characteristics, materials, and geometrical properties. On the other hand, to the peculiarities and uniqueness of artworks and artistic goods present on structural elements. In this paper, an AHP (absolute) model is proposed to rank multi-criteria prioritization of protection and restoration interventions on a set of 15 churches, which were damaged by earthquakes, occurring in Italy in the last decades. In detail, in order to structure the decision problem, identify key factors, and define the hierarchy, we conducted an extensive literature review and interviewed a pool of experts. Focus groups were organized to develop the set of criteria and sub-criteria and validate the hierarchy by dynamic discussion

Inconsistency evaluation in pairwise comparison using norm-based distances

AbstractThis paper studies the properties of an inconsistency index of a pairwise comparison matrix under the assumption that the index is defined as a norm-induced distance from the nearest consistent matrix. Under additive representation of preferences, it is proved that an inconsistency index defined in this way is a seminorm in the linear space of skew-symmetric matrices and several relevant properties hold. In particular, this linear space can be partitioned into equivalence classes, where each class is an affine subspace and all the matrices in the same class share a common value of the inconsistency index. The paper extends in a more general framework some results due, respectively, to Crawford and to Barzilai. It is also proved that norm-based inconsistency indices satisfy a set of six characterizing properties previously introduced, as well as an upper bound property for group preference aggregation

A Survey on Metric Learning for Feature Vectors and Structured Data

The need for appropriate ways to measure the distance or similarity between data is ubiquitous in machine learning, pattern recognition and data mining, but handcrafting such good metrics for specific problems is generally difficult. This has led to the emergence of metric learning, which aims at automatically learning a metric from data and has attracted a lot of interest in machine learning and related fields for the past ten years. This survey paper proposes a systematic review of the metric learning literature, highlighting the pros and cons of each approach. We pay particular attention to Mahalanobis distance metric learning, a well-studied and successful framework, but additionally present a wide range of methods that have recently emerged as powerful alternatives, including nonlinear metric learning, similarity learning and local metric learning. Recent trends and extensions, such as semi-supervised metric learning, metric learning for histogram data and the derivation of generalization guarantees, are also covered. Finally, this survey addresses metric learning for structured data, in particular edit distance learning, and attempts to give an overview of the remaining challenges in metric learning for the years to come.Comment: Technical report, 59 pages. Changes in v2: fixed typos and improved presentation. Changes in v3: fixed typos. Changes in v4: fixed typos and new method